Land-Cover and Land-Use Change in the Southern Yucat n Peninsular Region Project

2. Major Participants George Perkins Marsh Institute & Graduate School of Geography - Clark University Harvard Forest - Harvard University El Colegio de la Fronter Sur (ECOSUR) with cooperation of Center for Integrated Studies-Carnegie Mellon University For a list of individual researchers and their contributions to the project see the SYPR web page (earth.clarku.edu/lcluc)

3. Funding Sources Overall Project NASA - LCLUC Program ECOSUR Center for Integrated Studies, Carnegie Mellon University Ecological Research Harvard Forest Conservation Research Foundation National Science Foundation Modeling Research Center for Integrated Studies, Carnegie Mellon University NASA New Investigator Fellowship Specific Dissertation Research Fulbright Fellowship (Garcia Robles) NASA Earth Systems Science Fellowships (2) NSF Geography/Regional Science & Decision, Risk, and Management Science/INT-Americas Doctoral Dissertation Improvement Grants (3) InterAmerican Foundation Fellowship

4. Problem Overview LCLUC-SYPR seeks to identify the trajectories and pace of various land-use/cover changes in the region to develop detailed understanding and explanations of causes and consequences of these changes to develop a suite of models capable of explaining and projecting spatially explicit use and cover changes to do all the above through integrative of studies employing historical-empirical narratives, behavioral, structural and ecological theory, and remote sensing and GIS analysis.

5. Specific Goals and Components of LCLUC-SYPR Understand the dynamics of land-use/cover changes in SYPR, especially deforestation and agriculture since the 1960s Via the following questions: How does the historical narrative of the land-use/cover changes and the causes and consequences of these changes affect understanding, explanation, and modeling the current conditions of SYPR? What is the explanatory role of agent-based and structural-based theories, and can they be merged to improve understanding of land change? What is relation between market and non-market production among households and does �getting this relationship right� improve explanations of land change?

6. Why is chili production apparently succeeding, and what is its longer term economic viability and land impacts? Does the presence of different resources institutions (rules of access) matter in terms of land-use/cover conditions? What is the structure and function of the three main forest types present in SYPR? What is the role of nutrient cycling in forest succession and land-use decisions? What is the role of hurricanes in land-use/cover change? Can TM imagery analysis be pushed to the level of specification needed for detailed land-change studies? Does spatial explicitness improve or hinder explanation.

7. Specific Goals of LCLUC-SYPR Develop and test the applicability of three types of spatially explicit models that explain and project land-use/cover changes, especially for forest and agriculture. Disaggregate household & ejido models using survey, census, and environmental information linked to remotely sensed imagery (pixelizing the social = Focus 1 research) Aggregate imagery-based models built from TM imagery but incorporating biophysical and social information (socializing the pixel = Focus 2 research) Dynamic spatial simulation models used to project land-use/cover changes under different scenarios (Focus 4 research)

10. Environmental Schema of Region

12. Focus 3: Environmental Assessment and Reconstruction leading to the ecological consequence of and impacts on land change The aims of this work are: [1] to identify and measure the principal biophysical perturbations on SYPR forests, [2] identify the basic structures of the different forest types and successional growth, [3] determine the role nutrient cycling on use and forest, and [4] incorporate these dynamics into the various SYPR land-change models. ATTENTION: Slides marked by * are not be used or cited without permission of the project.

16. Composition, Structure, Dynamics, and Regeneration of Forests: SYPR study sites**

17. Composition, Structure, Dynamics, and Regeneration of Vegetation Six vegetation sites established across the region representing gradient of precipitation, soil depth, topography, and hurricane exposure. Minimally 10 vegetation plots (circular 500 m2 each) per forest type at each site. Tree (>10 cm dbh) and liana species composition and structural characteristics of forest record for each plot (as well as epiphyte load). In nested 100 m2, stems >5 cm dbh or >2 m in height are recorded. Seedling layer to be investigated in future.

19. Changes in Nutrient Cycling During Succession

21. Linking Biomass and Species Composition to Nutrient Cycling Processes

22. Variation in Forest Floor Mass vs. Age*[Across Study Area]

23. Variation in Monthly Litter Fall vs. Age* [Across Study Area]

24. Focus 1: Historical Narrative and Socio-Economic Conditionleading to econometric analysis and theory-based models Critical aims are to determine: [1] the influence of previous political economic conditions on those now present, [2] the rationality of semi-subsistence land managers and its land management implications, and [3] the institutional and infrastructural conditions in which managers operate, and [4] to create models that move from the �magnitude� of change to the �specific locations� of the change. ATTENTION: Slides marked by (*) are not be used or cited without permission of the project.

30. Smallholders, Infrastructures, and Institutions �rationality� of land users conditions in which they operate land management practices

32. LCLUC-SYPR: Example Question Guiding Theory Construction and Household Surveys To what extent is the expansion of agriculture (i.e., forest to open land) propelled by: [1] On-site demands for subsistence crops or [2] External markets for commercial crops?

33. Predominant Land Uses in SYPR: Tentative Survey Results*

34. Indicators of Economic Links to Subsistence & Market Production*

35. Spatially Explicit Land Models Previous spatially-explicit econometric models Aggregate socioeconomic data Profit maximizing behavior LCLUC-SYPR Project Individual land manager data linked to the pixel Test hypotheses concerning hh behavior linked to structural and infrastructural changes

38. Separability of Production and Consumption by Farmstead Type [sell maize or not]:Non-linear Specification of Demographic Variables*

39. Focus 2: TM Imagery Analysis, GIS and theRegional Conditionleading to empirical diagnostic models

40. Target Classification Scheme

41. Image Processing Methodology: Overview

42. Noise Removal of Landsat TM: DEHAZING

43. Noise Removal of Landsat TM: Principal Components Analysis Perform PCA on 6 of 7 Landsat TM bands (no thermal band) Results: striping and noise eliminated, data redundancy reduced

44. Incorporating Spatial Context:Texture Analysis* Image texture -- distinctive spatial and spectral relationships among neighboring pixels Focus on overall pattern of variation in each category, rather than spectral average Texture Analysis on PCA results: (1) improved separability of upland and lowland forests, cropland and pasture, transitional edges, (2) allowed the detection and separation of 4 classes of secondary (successional) vegetation

45. Improving Signature Separability Using Texture*

46. Compilation of PCA, Texture and NDVI Bands Normalized Difference Vegetation Index (NDVI) is a measure of relative biomass NDVI = (IR-R)/(IR+R) 3 PCA bands, 3 Texture Bands, 1 NDVI band layer-stacked to produce final 7-band image for signature development and classification

47. Improving Classification Contingency Using Texture & NDVI*

51. Characterizing Uncertainty: Soft Classifiers*

52. Example Classification for Modeling Land-Use/Cover Change

53. Modeling Approaches Disaggregate, household and ejido-based Aggregate, imagery-based Dynamic spatial simulation

56. Trial Model of Land-Use/Cover Change Deforestation: Land-cover changes from Forest (mature and successional) to Cropland/Pasture Succession: Land-cover changes from Cropland/Pasture to young secondary vegetation Model 2 time periods: 1988-1992, 1992-1995

57. Explanatory Variables Biophysical: soils, topography, initial cover class Locational and infrastructure: distances to roads, village centers, markets, nearest other land cover Landscape pattern indices: fragmentation, diversity, richness, number of different classes (NDC) Socioeconomic: demographic, wealth indicators

58. Biophysical Variables

59. Locational and Infrastructure Variables

60. Landscape Pattern Indices

61. Demographic and Socioeconomic Variables

62. Model Estimation Binomial LOGIT model Endogenous Variable: Forest Persistence vs. Deforestation Exogenous Variables: GIS layers Base Case: Forest Persistence Estimate Parameters evaluate significance of exogenous variables in model explanation enable prediction of deforestation probability

63. Model Output: Deforestation in 1988-92 Logit Estimates Number of obs = 805898 chi2(21) = 147541.4 > 5 Prob > chi2 = 0.0000 Log Likelihood = -219751.11 Pseudo R2 = 0.2513 depen| Coef. Std. Err. z P>|z| [95% Conf. Interval] elev | -.021297 .0002431 -87.615 0.000 -.0217734 -.0208206 slop | .0366753 .0024783 14.799 0.000 .0318179 .0415327 sl2 | -.6190486 .0247161 -25.046 0.000 -.6674912 -.5706059 sl3 | -.5856845 .0091304 -64.146 0.000 -.6035798 -.5677892 baj | -1.043225 .0263979 -39.519 0.000 -1.094964 -.9914857 med | -.2162192 .0251993 -8.580 0.000 -.265609 -.1668295 sec2 | .3272907 .0245467 13.333 0.000 .27918 .3754013 sec3 | -.7267448 .0513751 -14.146 0.000 -.8274381 -.6260514 disr | -.0205669 .0002015 -102.072 0.000 -.0209619 -.020172 dism | .0006189 .0001467 4.218 0.000 .0003313 .0009065 disv | .3046899 .0184065 16.553 0.000 .2686137 .340766 disc | -.0008068 .0000101 -79.685 0.000 -.0008267 -.000787 div | .1112383 .0127281 8.740 0.000 .0862917 .1361849 rich | -.000519 .0015288 -0.339 0.734 -.0035155 .0024775 pop | -.0024866 .0000777 -32.011 0.000 -.0026388 -.0023343 popc | 3.414536 .1871055 18.249 0.000 3.047816 3.781256 pden | 23.44893 .5243971 44.716 0.000 22.42113 24.47673 cden | -2.321342 .093875 -24.728 0.000 -2.505334 -2.13735 cdc | 9.393413 .5876295 15.985 0.000 8.24168 10.54515 tden | 172.7646 8.729288 19.791 0.000 155.6555 189.8737 tdc | -5.90372 .5648981 -10.451 0.000 -7.0109 -4.79654 _cons | 2.112397 .0502639 42.026 0.000 2.013882 2.210913

64. Spatially Explicit Probability Maps*

65. �Hardening� Predictions*

67. Results of Binomial LOGIT Model

68. Kappa Index of Agreement Statistics*

69. Focus 2: Summary High level of detail extracted from Landsat TM experimental image processing techniques exhaustive multi-date training sites from land-use histories detailed classification of cover and succession Model Estimation use of spatially explicit endogenous & exogenous variables probabilistic prediction maps Model Validation and Future Directions Optimization of threshold for probability of change Kappa statistics to Relative Operating Characteristic [ROC] Dynamic transition probabilities and projections Incorporate spatially explicit decision-making rules derived from Focus 1 household model as additional exogenous variables Incorporate constraints on land use (change): e.g., forest reserves Incorporate spatially explicit ecological feedbacks derived from Focus 3 on future land use/cover

70. A conceptual actor-institution-environment framework is mapped onto a computer model to form an agent-based dynamics spatial simulation The conceptual framework joins: Actors: agrarian decision making interpreted by bounded rationality & resource profiles Institutions: land tenure & subsidies Environment: simple ecological relationships

71. Conceptual Model Relationships

72. Assertions and Model Configurations Different forms of decision making: actor behavior as simple rules vs. group-based rules Actor heterogeneity: a single kind of smallholder-agent vs. different classes of agents Institutions: no institutions vs. an array of institutions Reactive environment: unchanging landscape vs. an adaptive, reactive environment with endogenous transition rules

73. ADSS Implementation

74. Challenges Dimensionality: time, space & scale Construction: calibration (esp. agents), validation, visualization & software integration Conceptual barriers: adequacy of conceptual model, uncertainty, qualitative knowledge & ethics

75. Added Elements of Study Forest reconstruction from aerial photography Household fertility and education study Resource institution typology Landscape fragmentation assessment Chile management strategies Origins and structure of chile production Forest succession and management practices